U-YOLO: higher precision YOLOv4

Hui Chen; Shuai Sun

doi:10.1117/12.2589437

27 January 2021 U-YOLO: higher precision YOLOv4

Hui Chen, Shuai Sun

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172007 (2021) https://doi.org/10.1117/12.2589437
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

YOLO is a milestone algorithm of object detection, which is the first One-stage detector in deep learning era. In spite of its great improvement of detection speed, the detection accuracy is somewhat insufficient, especially for small targets. In this paper, U-shaped module based on YOLOv4 (U-YOLO) is proposed. First, multi-level features extracted by CSPDarknet using Feature Pyramid Network (FPN) are fused. Then, the fused features is fed into multiple U-shaped modules. Finally, feature maps consisting of the features from different U-shaped modules are gathered up to construct a feature pyramid for object detection. Experiment shows that the U-shaped module can improve the accuracy of YOLOv4.

Citation Download Citation

Hui Chen and Shuai Sun "U-YOLO: higher precision YOLOv4", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172007 (27 January 2021); https://doi.org/10.1117/12.2589437

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